Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change

Abstract

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

Analysis of Massive Imports of Open Data in Openstreetmap Database: A Study Case for France

“…Since then, VGI has been the subject of numerous research studies, relative to both the characteristics of the data and the dynamics of the communities of contributors. In particular, the interactions between VGI and traditional spatial data produced by official agencies have led to studies with the goal to compare it to authoritative spatial data to assess their quality, where much review papers are published (Senaratne et al, 2017;Yan et al, 2020) and update or enrich these authoritative spatial data (Liu et al, 2015;Ivanovic et al, 2020;Olteanu-Raimond et al, 2020). Among the many forms VGI can take and the many projects based on VGI as mentioned by See et al (2016), the Open-StreetMap (OSM) project is one of the most prominents.…”

Metropolitan Research. Methods and Approaches

“…Hence, the potential for applying VGI for monitoring the SDGs, which are the "blueprint to achieve a better and more sustainable future for all" (UN 2020) is eo ipso given. Recent studies apply VGI for flood impact analysis (Barz et al 2019), understanding patterns of social segregation (Taubenböck et al 2018), and the creation of land use classifications (Olteanu-Raimond et al 2020). The DFG SPP 1894 "Volunteered Geographic Information: Interpretation, Visualisierung und Social Computing", e.g., is focusing on a sustainable use of VGI software elements and their analytical possibilities.…”

Remote Sensing for Short-Term Economic Forecasts

“…Here, specific change detection approaches are necessary (e.g., Radke et al, 2005 [23], Olteanu-Raimond et al, 2020 [24], Henits et al, 2016 [25]). Juergens and Meyer-Heß (2021) [26] worked with finer spatial resolution and reported on their findings related to construction areas based on mono-temporal VHR satellite images and combined change detection analysis.…”